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Our Latest R&D OutputI’m excited today to announce the latest output from our R&D pipeline: of the and —available immediately on desktop (Mac, Windows, Linux) and cloud.It was only this spring that we released. But after the summer we’re now ready for another impressive release—with all kinds of additions and enhancements, including 100+ entirely new functions:We have a very deliberate strategy for our releases. Integer releases (like 11) concentrate on major complete new frameworks that we’ll be building on far into the future. (like 11.2) are intended as snapshots of the latest output from our R&D pipeline–delivering new capabilities large and small as soon as they’re ready.Version 11.2 has a mixture of things in it—ranging from ones that provide finishing touches to existing major frameworks, to ones that are first hints of major frameworks under construction.
One of my personal responsibilities is to make sure that everything we add is coherently designed, and fits into the long-term vision of the system in a unified way.And by the time we’re getting ready for a release, I’ve been involved enough with most of the new functions we’re adding that they begin to feel like personal friends. So when we’re doing a.1 release and seeing what new functions are going to be ready for it, it’s a bit like making a party invitation list: who’s going to be able to come to the big celebration?Years back there’d be a nice list, but it would be of modest length. Today, however, I’m just amazed at how fast our R&D pipeline is running, and how much comes out of it every month. Yes, we’ve been consistently building our Wolfram Language technology stack for more than 30 years—and we’ve got a great team.
But it’s still a thrill for me to see just how much we’re actually able to deliver to all our users in a.1 release like 11.2. Advances in Machine LearningIt’s hard to know where to begin. But let’s pick a current hot area:.We’ve had functionality that would now be considered machine learning in the Wolfram Language for decades, and back in 2014 we introduced the “machine-learning superfunctions” and —to give broad access to modern machine learning. By early 2015, we had state-of-the-art deep-learning image identification in, and then, last year, in Version 11, we began rolling out our full symbolic.Our goal is to push the envelope of what’s possible in machine learning, but also to deliver everything in a nice, integrated way that makes it easy for a wide range of people to use, even if they’re not machine-learning experts. And in Version 11.2 we’ve actually used machine learning to add automation to our machine-learning capabilities.So, in particular, and are significantly more powerful in Version 11.2. Their basic scheme is that you give them training data, and they’ll learn from it to automatically produce a machine-learning classifier or predictor.
But a critical thing in doing this well is to know what features to extract from the data—whether it’s images, sounds, text, or whatever. And in Version 11.2 and have a variety of new kinds of built-in feature extractors that have been pre-trained on a wide range of kinds of data.But the most obviously new aspect of and is how they select the core machine-learning method to use (as well as hyperparameters for it). (By the way, 11.2 also introduces things like optimized gradient-boosted trees.) And if you run and now in a notebook you’ll actually see them dynamically figuring out and optimizing what they’re doing (needless to say, using machine learning):By the way, you can always press Stop to stop the training process. And with the new option you can explicitly say how long the training should be planned to be—from seconds to years.As a field, machine learning is advancing very rapidly right now (in the course of my career, I’ve seen perhaps a dozen fields in this kind of hypergrowth—and it’s always exciting).
And one of the things about our general symbolic neural net framework is that we’re able to take new advances and immediately integrate them into our long-term system—and build on them in all sorts of ways.At the front lines of this is the function —to which new trained and untrained models are being added all the time. (The models are hosted in the cloud—but downloaded and cached for desktop or embedded use.) And so, for example, a few weeks ago got a new model for inferring geolocations of photographs—that’s based on from just a few months ago. ✕ Classify'NSFWImage',CloudGet'built-in function (whose underlying network you can access with ) has been tuned and retrained for Version 11.2—but fundamentally it’s still a classifier.
One of the important things that’s happening with machine learning is the development of new types of functions, supporting new kinds of workflows. We’ve got a lot of development going on in this direction, but for 11.2 one new (and fun) example is —that takes a picture and applies the style of another picture to it. ✕ Limit(3 x + Sqrt9 x^2 + 4 x - Sinx), x - -InfinityIt’s very convenient that we have a test set of millions of complicated limit problems that people have asked about over the past few years—and I’m pleased to say that with our new algorithms we can now immediately handle more than 96% of them.Limits are in a sense at the very core of calculus and continuous mathematics—and to do them correctly requires a huge tower of knowledge about a whole variety of areas of mathematics. Multivariate limits are particularly tricky—with the main takeaway from many textbooks basically being “it’s hard to get them right”. Well, in 11.2, thanks to our new algorithms (and with a lot of support from our algebra, functional analysis and geometry capabilities), we’re finally able to correctly do a very wide range of multivariate limits—saying whether there’s a definite answer, or whether the limit is provably indeterminate.Version 11.2 also introduces two other convenient mathematical constructs: and (sometimes known as lim sup and lim inf). Ordinary limits have a habit of being indeterminate whenever things get funky, but and have definite values, and are what come up most often in applications.So, for example, there isn’t a definite ordinary limit here.
✕ GeoListPlot%Another thing that’s new in 11.2 is greatly enhanced predictive caching of data in the Wolfram Language—making it much more efficient to compute with large volumes of curated data from the Wolfram Knowledgebase.By the way, Version 11.2 is the first new version to be released since the was launched. And through the Data Repository, 11.2 has access to nearly 600 curated datasets across a very wide range of areas. 11.2 also now supports functions like, for programmatically submitting data for publication in the Wolfram Data Repository. (You can also publish data yourself just using.)There’s a huge amount of data and types of computations available in Wolfram Alpha—that with great effort have been brought to the level where they can be relied on, at least for the kind of one-shot usage that’s typical in Wolfram Alpha. But one of our long-term goals is to take as many areas as possible and raise the level even higher—to the point where they can be built into the core Wolfram Language, and relied on for systematic programmatic usage.In Version 11.2 an area where this has happened is ocean tides. So now there’s a function that can give tide predictions for any of the tide stations around the world.
I actually found myself using this function in a recent livecoding session I did—where it so happened that I needed to know daily water levels in Aberdeen Harbor in 1913. (Watch the to find out why!). ✕ Volume%More AudioVersion 11 introduced a major new framework for large-scale audio processing in the Wolfram Language. We’re still developing all sorts of capabilities based on this framework, especially using machine learning. And in Version 11.2 there are a number of immediate enhancements. There are very practical things, like built-in support for under Linux.
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There’s also now the notion of a dynamic, whose playback can be programmatically controlled.Another new function is, which creates audio from text. ✕ MemoryAvailable Unified Asynchronous TasksThere’ve been ways to do some kinds of asynchronous or “background” tasks in the Wolfram Language for a while, but in 11.2 there’s a complete systematic framework for it. There’s a thing called that symbolically represents an asynchronous task. And there are basically now three ways such a task can be executed. First, there’s, which submits the task for execution in the cloud.
Then there’s, which submits the task to be executed on your local computer, but in a separate subkernel. And finally, there’s, which executes the task in idle time in your current Wolfram Language session.When you submit a task, it’s off getting executed (you can schedule it to happen at particular times using ). The way you “hear back” from the task is through “handler functions”: functions that are set up when you submit the task to “handle” certain events that can occur during the execution of the task (completion, errors, etc.).There are also functions like, and so on, that let you interact with tasks “from the outside”.
And, yes, when you’re doing big machine-learning trainings, for example, this comes in pretty handy. ConnectivityWe’re always keen to make the Wolfram Language as connected as it can be. And in Version 11.2 we’ve added a variety of features to achieve that. In Version 11 we introduced the option, which lets you give credentials in functions like.
Version 11 already allowed for (a.k.a. An “app id”).
In 11.2 you can now give an explicit username and password, and you can also use to provide OAuth credentials. It’s tricky stuff, but I’m pleased with how cleanly we’re able to represent it using the symbolic character of the Wolfram Language—and it’s really useful when you’re, for example, actually working with a bunch internal websites or APIs.Back in Version 10 (2014) we introduced the very powerful idea of using to provide a symbolic specification for a web API—that could be deployed to the cloud using. Then in Version 10.2 we introduced, which responds not to web requests, but instead to receiving mail messages. (By the way, in 11.2 we’ve considerably strengthened, notably adding various authentication and address validation capabilities.)In Version 11, we introduced the channel framework, which allows for publish-subscribe interactions between Wolfram Language instances (and external programs)—enabling things like chat, as well as a host of useful internal services.
Well, in our continual path of automating and unifying, we’re introducing in 11.2 —which can be deployed to the cloud to respond to whatever messages are sent on a particular channel.In the low-level software engineering of the Wolfram Language we’ve used sockets for a long time. A few years ago we started exposing some socket functionality within the language. And now in 11.2 we have a full socket framework.
The socket framework supports both traditional TCP sockets, as well as modern ZeroMQ sockets. External ProgramsEver since the beginning, the Wolfram Language has been able to communicate with external C programs—actually using its native symbolic expression transfer protocol. Years ago and enabled seamless connection to Java and.Net programs. Did the same for R. Then there are things like, that allow direct connection to DLLs—or for running programs from the shell.But 11.2 introduces a new form of external program communication:. Is for doing computation in languages which—like the Wolfram Language—support REPL-style input/output. The two first examples available in 11.2 are Python and NodeJS.Here’s a computation done with NodeJS—though this would definitely be better done directly in the Wolfram Language.
✕ ExternalEvaluate'Python', ' i & 10 for i in range(10)'Of course, the place where things start to get useful is when one’s accessing large external code bases or libraries. And what’s nice is that one can use the Wolfram Language to control everything, and to analyze the results. Is in a sense a very lightweight construct—and one can routinely use it even deep inside some piece of Wolfram Language code.There’s an infrastructure around, aimed at connecting to the correct executable, appropriately converting types, and so on. There’s also, which allows you to start a single external session, and then perform multiple evaluations in it. The Whole ListSo is there still more to say about 11.2? There are lots of new functions and features that I haven’t mentioned at all. Here’s a:But if you want to find out about 11.2, the best thing to do is to actually.
I’ve actually been running pre-release versions of 11.2 on my personal machines for a couple of months. So by now I’m taking the new features and functions quite for granted—even though, earlier on, I kept on saying “this is really useful; how could we have not had this for 30 years?”. Well, realistically, it’s taken building everything we have so far—not only to provide the technical foundations, but also to seed the ideas, for 11.2. But now our work on 11.2 is done, and 11.2 is ready to go out into the world—and deliver the latest results from our decades of research and development.
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